Artificial intelligence profiling

ABSTRACT

Technical solutions are described for controlling an artificial intelligent gaming device. For example, a computer-implemented method includes identifying an electronic opponent profile for an opponent. The computer-implemented method also includes selecting, from a profile repository, a first set of robot profiles, where robots trained using robot profiles from the first set of robot profiles have previously defeated the opponent. The computer-implemented method also includes selecting, from the profile repository, a second set of robot profiles, where robots trained using robot profiles from the second set of robot profiles have previously lost to the opponent. The computer-implemented method also includes generating a current robot profile based on the first set of robot profiles and the second robot profiles. The computer-implemented method also includes configuring a robot according to the current robot profile to play against the opponent.

BACKGROUND

The present invention relates to computer technology and moreparticularly to, artificial intelligence (AI) systems to adapt topersonality traits of human players, in addition to game play.

Artificial intelligence aims to produce a machine that exhibitscharacteristics associated with human intelligence, such as languagecomprehension, problem solving, pattern recognition, learning, andreasoning from incomplete or uncertain information. Typically, AIsystems rely on faster computer hardware, larger memories, databases,and knowledge bases to act as expert systems that perform well atspecific tasks, such as playing chess or diagnosing medical conditions,as long as the procedures and objectives are precisely defined and donot change.

SUMMARY

Embodiments of the present invention are directed to acomputer-implemented method for controlling an artificial intelligentgaming device. A non-limiting example of the computer-implemented methodincludes identifying an electronic opponent profile for an opponent. Thecomputer-implemented method also includes selecting, from a profilerepository, a first set of robot profiles, where robots trained usingrobot profiles from the first set of robot profiles have previouslydefeated the opponent. The computer-implemented method also includesselecting, from the profile repository, a second set of robot profiles,where robots trained using robot profiles from the second set of robotprofiles have previously lost to the opponent. The computer-implementedmethod also includes generating a current robot profile based on thefirst set of robot profiles and the second robot profiles. Thecomputer-implemented method also includes configuring a robot accordingto the current robot profile to play against the opponent.

Embodiments of the present invention are directed to a system forcontrolling an artificial intelligent gaming device. A non-limitingexample of the system includes a computer-game system including amemory, a robot configured to play a game against an opponent, and aprocessor coupled with the memory and the robot. The processoridentifies an electronic opponent profile for the opponent. Theprocessor further selects, from a profile repository, a first set ofrobot profiles, where robots trained using robot profiles from the firstset of robot profiles have previously defeated the opponent. Theprocessor further selects, from the profile repository, a second set ofrobot profiles, where robots trained using robot profiles from thesecond set of robot profiles have previously lost to the opponent. Theprocessor further generates a current robot profile based on the firstset of robot profiles and the second robot profiles. The processorfurther configures the robot according to the current robot profile toplay against the opponent.

Embodiments of the invention are directed to a computer program productfor controlling an artificial intelligent gaming device, the computerprogram product comprising a computer readable storage medium havingprogram instructions embodied therewith. The program instructions areexecutable by a processor to cause the processor to perform a method fortraining a robot for gameplay. A non-limiting example of the methodincludes identifying an electronic opponent profile for an opponent. Themethod further includes selecting, from a profile repository, a firstset of robot profiles, where robots trained using robot profiles fromthe first set of robot profiles have previously defeated the opponent.The method further includes selecting, from the profile repository, asecond set of robot profiles, where robots trained using robot profilesfrom the second set of robot profiles have previously lost to theopponent. The method further includes generating a current robot profilebased on the first set of robot profiles and the second robot profiles.The method further includes configuring a robot according to the currentrobot profile to play against the opponent.

Additional technical features and benefits are realized through thetechniques of the present invention. Embodiments and aspects of theinvention are described in detail herein and are considered a part ofthe claimed subject matter. For a better understanding, refer to thedetailed description and to the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The specifics of the exclusive rights described herein are particularlypointed out and distinctly claimed in the claims at the conclusion ofthe specification. The foregoing and other features and advantages ofthe embodiments of the invention are apparent from the followingdetailed description taken in conjunction with the accompanying drawingsin which:

FIG. 1 depicts a cloud computing environment according to an embodimentof the present invention;

FIG. 2 depicts abstraction model layers according to an embodiment ofthe present invention;

FIG. 3 depicts a computer gaming system 100 according to embodiments ofthe invention;

FIG. 4 depicts an example robot, according to one or more embodiments ofthe present invention;

FIG. 5 illustrates a flowchart of an example method for a computergaming system to train robot models, according to one or moreembodiments of the present invention;

FIG. 6 illustrates a flowchart of an example method for generating arobot profile using the mapping data, according to one or moreembodiments of the present invention; and

FIG. 7 illustrates a flowchart of an example method for generating arobot profile for a new opponent, according to one or more embodimentsof the present invention.

The diagrams depicted herein are illustrative. There can be manyvariations to the diagram or the operations described therein withoutdeparting from the spirit of the invention. For instance, the actionscan be performed in a differing order or actions can be added, deletedor modified. Also, the term “coupled” and variations thereof describeshaving a communications path between two elements and does not imply adirect connection between the elements with no interveningelements/connections between them. All of these variations areconsidered a part of the specification.

In the accompanying figures and following detailed description of thedisclosed embodiments, the various elements illustrated in the figuresare provided with two or three digit reference numbers. With minorexceptions, the leftmost digit(s) of each reference number correspond tothe figure in which its element is first illustrated.

DETAILED DESCRIPTION

Various embodiments of the invention are described herein with referenceto the related drawings. Alternative embodiments of the invention can bedevised without departing from the scope of this invention. Variousconnections and positional relationships (e.g., over, below, adjacent,etc.) are set forth between elements in the following description and inthe drawings. These connections and/or positional relationships, unlessspecified otherwise, can be direct or indirect, and the presentinvention is not intended to be limiting in this respect. Accordingly, acoupling of entities can refer to either a direct or an indirectcoupling, and a positional relationship between entities can be a director indirect positional relationship. Moreover, the various tasks andprocess steps described herein can be incorporated into a morecomprehensive procedure or process having additional steps orfunctionality not described in detail herein.

The following definitions and abbreviations are to be used for theinterpretation of the claims and the specification. As used herein, theterms “comprises,” “comprising,” “includes,” “including,” “has,”“having,” “contains” or “containing,” or any other variation thereof,are intended to cover a non-exclusive inclusion. For example, acomposition, a mixture, process, method, article, or apparatus thatcomprises a list of elements is not necessarily limited to only thoseelements but can include other elements not expressly listed or inherentto such composition, mixture, process, method, article, or apparatus.

Additionally, the term “exemplary” is used herein to mean “serving as anexample, instance or illustration.” Any embodiment or design describedherein as “exemplary” is not necessarily to be construed as preferred oradvantageous over other embodiments or designs. The terms “at least one”and “one or more” may be understood to include any integer numbergreater than or equal to one, i.e. one, two, three, four, etc. The terms“a plurality” may be understood to include any integer number greaterthan or equal to two, i.e. two, three, four, five, etc. The term“connection” may include both an indirect “connection” and a direct“connection.”

The terms “about,” “substantially,” “approximately,” and variationsthereof, are intended to include the degree of error associated withmeasurement of the particular quantity based upon the equipmentavailable at the time of filing the application. For example, “about”can include a range of ±8% or 5%, or 2% of a given value.

For the sake of brevity, conventional techniques related to making andusing aspects of the invention may or may not be described in detailherein. In particular, various aspects of computing systems and specificcomputer programs to implement the various technical features describedherein are well known. Accordingly, in the interest of brevity, manyconventional implementation details are only mentioned briefly herein orare omitted entirely without providing the well-known system and/orprocess details.

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 1, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 comprises one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 1 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 2, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 1) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 2 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and game playing 96.

Turning now to an overview of technologies that are more specificallyrelevant to aspects of the invention, artificial intelligence systems,such as a computer chess system, employed a combination ofcomputationally intensive brute-force searching of possible chesspositions for several moves ahead, sophisticated scoring and searchingheuristics, and a database of openings and end-games that was able todefeat the world's top-rated human chess player at the time. What chessaudiences had witnessed, however, was merely a triumph of brute forcecomputation to solve a particular problem, not a feat of generalintelligence. The technical solutions herein, in addition to using suchheuristics, facilitate using human personality data in order to defeatan opponent. Thus, the technical solutions use a combination of gamestrategy and the human personality (both defined in mathematical forms)in order to train one or more game-playing models and evolve into betterpersonalities to defeat an opponent. For example, the technicalsolutions facilitate the game-playing model to defeat an opponent, suchas a human player, considering one-to-one relations between two players(instead of transitive relations) and uses them to create evolvedpersonalities which are more likely to defeat the opponent. Further, thetechnical solutions facilitate collaborating independent AI systems toshare intelligence and evolve, to defeat opponents over time.

It should be noted that while chess and/or poker are used in theexamples described herein, the technical solutions herein are notlimited to those games, rather the technical solutions are applicable toany artificial intelligence system used to play a game against anotherplayer, such as draughts, checker, etc.

Turning now to an overview of the aspects of the invention, one or moreembodiments of the invention address the above-described shortcomings ofthe prior art by providing, irrespective of the game being played,techniques for training one or more AI models using a game strategy,such as the AI based logic typically seen online in the form of onlinepoker, chess etc., and in addition facilitates training the models usingthe human personality traits, to defeat human opponents. In one or moreexamples, the technical solutions define the human personality in formof mathematical terms and use it to train and evolve the AI models.Further, the technical solutions train independent AI models in anon-transitive way (A defeat B and B defeat C does not mean A defeat Cbecause of personality issues). Further yet, the technical solutionsfacilitate the independent models to collaborate to defeat an opponent.

The above-described aspects of the invention address the shortcomings ofthe prior art by making independent AI models collaborate to evolve intobetter personalities, which can defeat humans in aggressive games likepoker, chess etc., particularly by inclusion of human personality partin the form of mathematical terms and using it to train and evolve AImodels.

Turning now to a more detailed description of aspects of the presentinvention, FIG. 3 depicts a computer gaming system 100 according toembodiments of the invention. In one or more examples, the computergaming system 100 is cloud-based, for example, using layer 96 (FIG. 1).In one or more examples, the computer gaming system 100 includes a robot110 that plays a game, such as chess, poker, etc. with an opponent 120.In one or more examples, the opponent 120 is a human operator. The robot110 plays the game with the opponent 120 based on a robot profile 115.In one or more examples, the robot profile 115 is determined based on anopponent profile 125 of the opponent 120. In one or more examples, therobot 110 determines the robot profile 115 based on information accessedfrom a profile repository 130. The profile repository 130 stores amapping 140 between one or more robot profiles and human profiles thathave competed in the game previously. The mapping 140 also storesoutcomes of the previous games between the robot profiles and the humanprofiles.

FIG. 4 depicts an example robot 110, according to one or moreembodiments of the present invention. The robot 110 may be acommunication apparatus, such as a computer. For example, the robot 110may be a desktop computer, a tablet computer, a laptop computer, aphone, such as a smartphone, a server computer, or any other suchelectronic device. In one or more examples, the robot communicates via anetwork 265. The system 300 includes hardware, such as electroniccircuitry.

The robot 110 includes, among other components, a processor 205, memory210 coupled to a memory controller 215, and one or more input devices245 and/or output devices 240, such as peripheral or control devices,that are communicatively coupled via a local I/O controller 235. Thesedevices 240 and 245 include, for example, battery sensors, positionsensors, indicator/identification lights and the like. Input devicessuch as a conventional keyboard 250 and mouse 255 are coupled to the I/Ocontroller 235. The I/O controller 235 may be, for example, one or morebuses or other wired or wireless connections, as are known in the art.The I/O controller 235 may have additional elements, which are omittedfor simplicity, such as controllers, buffers (caches), drivers,repeaters, and receivers, to enable communications.

The I/O devices 240, 245 may further include devices that communicateboth inputs and outputs, for instance disk and tape storage, a networkinterface card (NIC) or modulator/demodulator (for accessing otherfiles, devices, systems, or a network), a radio frequency (RF) or othertransceiver, a telephonic interface, a bridge, a router, and the like.

The processor 205 is a hardware device for executing hardwareinstructions or software, particularly those stored in memory 210. Theprocessor 205 may be a custom made or commercially available processor,a central processing unit (CPU), an auxiliary processor among severalprocessors associated with the robot 110, a semiconductor basedmicroprocessor (in the form of a microchip or chip set), amacroprocessor, or other device for executing instructions. Theprocessor 205 includes a cache 270, which may include, but is notlimited to, an instruction cache to speed up executable instructionfetch, a data cache to speed up data fetch and store, and a translationlookaside buffer (TLB) used to speed up virtual-to-physical addresstranslation for both executable instructions and data. The cache 270 maybe organized as a hierarchy of more cache levels (L1, L2, and so on.).

The memory 210 includes one or combinations of volatile memory elements(for example, random access memory, RAM, such as DRAM, SRAM, SDRAM) andnonvolatile memory elements (for example, ROM, erasable programmableread only memory (EPROM), electronically erasable programmable read onlymemory (EEPROM), programmable read only memory (PROM), tape, compactdisc read only memory (CD-ROM), disk, diskette, cartridge, cassette orthe like). Moreover, the memory 210 incorporates electronic, magnetic,optical, or other types of storage media. Note that the memory 210 mayhave a distributed architecture, where various components are situatedremote from one another but may be accessed by the processor 205.

The instructions in memory 210 include one or more separate programs,each of which comprises an ordered listing of executable instructionsfor implementing logical functions. In the example of FIG. 2, theinstructions in the memory 210 include a suitable operating system (OS)211. The operating system 211 controls the execution of other computerprograms and provides scheduling, input-output control, file and datamanagement, memory management, and communication control and relatedservices.

Additional data, including, for example, instructions for the processor205 or other retrievable information, is stored in storage 220, which isa storage device such as a hard disk drive or solid state drive. Thestored instructions in memory 210 or in storage 220 include thoseenabling the processor to execute one or more aspects of the systems andmethods described herein.

The robot 110 further includes a display controller 225 coupled to auser interface or display 230. In some embodiments, the display 230 maybe an LCD screen. In other embodiments, the display 230 may include aplurality of LED status lights. In some embodiments, the robot 110further includes a network interface 260 for coupling to a network 265.The network 265 may be an IP-based network for communication between therobot 110 and an external server, client and the like via a broadbandconnection. In an embodiment, the network 265 may be a satellitenetwork. The network 265 transmits and receives data between the robot110 and external systems. In some embodiments, the network 265 may be amanaged IP network administered by a service provider. The network 265may be implemented in a wireless fashion, for example, using wirelessprotocols and technologies, such as Wi-Fi™, WiMAX™, satellite, or anyother. The network 265 may also be a packet-switched network such as alocal area network, wide area network, metropolitan area network, theInternet, or other similar type of network environment. The network 265may be a fixed wireless network, a wireless local area network (LAN), awireless wide area network (WAN) a personal area network (PAN), avirtual private network (VPN), intranet or other suitable network systemand may include equipment for receiving and transmitting signals.

Referring back to FIG. 3, although a single robot 110 and a singleopponent 120 are illustrated, it is understood that the computer gamingsystem 100 includes multiple robots and opponents 120 playing againsteach other. Further, the robot 110 may compete against multipleopponents simultaneously. Further yet, a single opponent 120 may becompeting against multiple robots simultaneously.

A profile, such as the opponent profile 125 depicts a computer readabledepiction of a game-player (robot 110, or opponent 120) personality. Inone or more examples, the robot 110 generates the opponent profile 125by capturing one or more observations about the opponent 120 during thegame play. For example, the robot 110 uses one or more input devicessuch as a camera, a microphone, biometric sensors (e.g. heart ratemonitor, iris monitor etc.) to monitor one or more out-of-game actionsof the opponent 120. In addition, the robot 110 monitors the in-gameactions of the opponent 120 based on input from opponent to interactwith the robot 110 to play the game. Based on the monitored traits ofthe opponent 120, the robot 110 generates the opponent profile 125. Inaddition, in one or more examples, the robot 110 uses a previousopponent profile from the profile repository 130, which it updates basedon the monitored traits of the opponent 120. The profile is anelectronic profile that can be stored in a memory device.

Table 1 illustrates an example opponent profile 125. In one or moreexamples, the depicted profile is for a game of poker. The depictedopponent profile 125 includes four variables forming two rows of a 2dmatrix: [g1 g2] and [r1 r2]. The first row [g1 g2] signifies theout-of-game actions of the opponent 120, while the second row [r1 r2]specifies a risk profile of the opponent 120 based on the in-gameactions of the opponent 120.

TABLE 1 g1 g2 t1 t2

For example, g1 specifies an aggressiveness of the opponent 120 based onout-of-game actions of the opponent 120. In one or more examples, theout-of-game action includes speech uttered by the opponent 120 duringthe game play. For example, the aggressiveness value is determined usinga neural network, accessed via an application-programming interface(API), such as IBM™ WATSON™ personality insights service. In one or moreexamples, the robot 110 converts the speech from the opponent into textand feeds the converted text into the API. In one or more examples, therobot 110 captures the speech from the opponent using a microphone, thespeech being provided as input to the API for determining theaggressiveness value g 1. It should be noted that the in other examples,the robot 110 determines the aggressiveness value using additional ordifferent out-of-game actions of the opponent 120.

In one or more examples, g2 specifies a confidence of the opponent 120that is predicted based on out-of-game actions of the opponent 120. Inone or more examples, the out-of-game actions include facial expressionsof the opponent 120 during game play. For example, the confidence valueis determined using a neural network, accessed via an API such as IBM™WATSON™ visual recognition service. In one or more examples, the robot110 captures still images, or video snippets of the opponent 120 using acamera, which are provided to the API to determine the confidence valueg2. It should be noted that the in other examples, the robot 110determines the confidence value using additional or differentout-of-game actions of the opponent 120.

Further, the values t1 and t2 in the example opponent profile 125 depictrisk parameters for the opponent 120 based on the in-game actions of theopponent 120. For example, in case the opponent profile 125 is for agame of poker, the values t1 and t2 respectively depict a lowerprobability threshold and a higher probability threshold associated withthe opponent 120. For example, in case the opponent 120 is dealt a handwith a probability of winning at or below the lower probabilitythreshold, the opponent 120 folds; and with a probability of winning ator above the lower probability threshold, the opponent 120 gambles alarger amount of money than average. In one or more examples, thecomputer gaming system 100 determines the risk parameters for theopponent using a custom neural networks service, such as using IBM™BLUEMIX™.

In one or more examples, the four variables are normalized to liebetween 0 to 1. In this case, the opponent profile 125 indicates theopponent's risk profile as a mathematical vector [t1 t2] where 0<t1,t2<1. Consider an example where the opponent 120 is a human playingpoker with the robot 110, where the opponent profile 120 indicates thehuman's risk taking personality is [0.3 0.5]. The opponent profile 125thus indicates that the human folds below 0.3, checks between 0.3 and0.5, and bets above 0.5. It should be noted that the vector indicatingthe risk parameters represent different attributes than the probabilitythresholds described herein in other examples. For example,extraversion, openness, neuroticism, agreeableness, conscientiousness.

In one or more examples, the robot 110 competing against the opponent120 asks the opponent one or more questions and records the answersprovided by the opponent. The answers are considered out-of-game actionsof the opponents. In addition, the robot 110 captures one or moreout-of-game actions of the opponent 120 while answering the questions,such as facial expressions, hand movements, perspiration, twitching, eyemovements, finger snapping, lip movements, etc. The robot 110 sends theout-of-game actions to the custom neural network service such as IBMWATSON™ BLUEIVIIX™, or the like to get scores associated with the one ormore personality traits, such as those listed above. The scores arerecorded in the opponent profile 125 in an electronic form to be used bythe robot 110 to determine in-game actions and to confuse the opponent120 during gameplay.

In one or more examples, the robot computes a risk-parameter thatrepresents risk-taking ability of the robot 110 based on the scoresassociated with the personality traits. For example, the risk takingability of the robot 110, provided by the robot profile 115, is based onextraversion, openness, neuroticism, and extraversion scores of theopponent 120. The personality traits scores of the opponent 120 arenormalized to a range such as 0 to 1. The computer gaming system 100classifies the personality traits scores using predetermined ranges,such as 3 ranges: 0.2-0.4, 0.6-0.7, 0.8-0.9. The predetermined rangesare used to randomly select values like (0.3, 0.65, 0.85) from eachrespective range, the selected values used by the robot 110 to determinean in-game action to play in the game. In one or more examples, therobot 110 provides the selected values as input to a game-play serviceto receive the in-game action to take in response from the game-playservice such as call the Poker service of IBM™ WATSON™ to play a game ofTexas Hold'em Poker. It should be noted that in other examples, adifferent game may be played, a different service may be used, anddifferent values may be selected as input, than those illustrated in theexample above.

Further yet, in one or more examples, the technical solutions hereinfacilitate defining the risk taking profile of the robot 110 usingranges, instead of constant values. For example, a robot 110 has thefollowing constants (0.3, 0.5, 0.7). Instead of assigning constantvalues to the robot 110 in the robot profile 115, the technicalsolutions herein define a range of constants: (0.10-0.40, 0.50-0.70,0.80-0.95). The ranges specify the limits from which the robot 110selects a set of constants say (0.25, 0.60, 0.85) to play in aparticular game, depending on the opponent 120 and correspondingopponent profile 125. The robot 110 thus gets the flexibility to selecta set of values by still being in the range of its risk limits, whichare defined by the ranges in the robot profile 125 (0.10-0.40,0.50-0.70, 0.80-0.95).

Each time the robot 110 plays or takes an in-game action, the robot 110selects a random value from the limits specified in the risk profile115: (0.10-0.40, 0.50-0.70, 0.80-0.95). The selected values are thenprovided to the play game service to determine the move to be made inthe game. By doing this, the robot 110 introduces an element ofuncertainty in the game and the opponent 120 is not able to predict amove by the robot over time. For example, table 2 lists example profiledata accumulated in the profile repository 130 for the robot profile115.

TABLE 2 Game Outcome (1 = robot win; Game Input Input Input 0 = robot #Value-1 Value-2 Value-3 lost) 1 0.27 0.50 0.86 0 2 0.25 0.58 0.88 1 30.28 0.50 0.85 0 4 0.25 0.57 0.89 1 5 0.28 0.54 0.85 0

The above example data is for the robot 110 selecting random inputvalues from the range in the robot profile 115 (0.10-0.40, 0.50-0.70,0.80-0.95) for different games played against a specific opponent 120.The data also includes outcomes of each of the games (Column 5).

In one or more examples, after the game is over, the robot 110 collectsat least the following information: a) opponent's actual game strength,for example hand-strength in poker (robot 110 determines this during thegame play) b) opponent's in-game actions recorded throughout the game(e.g., in case of poker, when the user checked, bet, and folded). Usingthe collected information, the robot 110 predicts the risk-takingability values of [t1 t2 . . . ] using the custom neural networksservice. For example, the computer gaming system 100 analyzes the abovedata from table 2 to determine (t1, t2, and t3) as the input-values touse based on the input-values that led the robot 110 to win against theopponent 120.

In the example opponent profile 125 of table 1, the two rows combine toform a 2d matrix depicting a personality of the opponent 120. It shouldbe noted that in other examples, additional or different values areincluded in the opponent profile 125.

Further, it should be noted that the neural networks used by thecomputer gaming system 100 are artificial neural networkimplementations, which include one or more neural networks such as afeedforward neural network, a radial basis function network, aconvolutional neural network, a recurrent neural network, a cascadingneural network, a spiking neural network, a neuro-fuzzy network, or anyother type of neural network implementation. With each newprofile-outcome recorded in the profile repository 130, the neuralnetwork layers get more precise, thus facilitating the computer gamingsystem 100 to predict whether the current robot profile 115 which therobot 110 has selected will lead it to win or not. Based on thisclassification, the robot 110 is configured with the robot profile 115,which has a higher probability of winning.

FIG. 5 illustrates a flowchart of an example method for the computergaming system 100 to train robot models, according to one or moreembodiments of the present invention. The computer gaming system 100trains multiple robot models by having multiple robots 110 to play withmultiple opponents 120. Consider n robots 110 (connected to a customneural network, such as BLUEMIX™ via a network, or via the internet ofthings (IoT)). The robots 110 each have respective robot profiles 115R1, R2 . . . Rn (say n=10). Further, consider that the robots 110compete against opponents 120 with opponent profiles H1, H2 . . . Hm(say m=50). The computer gaming system 100 initiates all of the robots110 with respective robot profiles 115. Each robot profile 115 of the nrobots 110 is different from each other, so that each of the robots 110has a different personality, and in turn, each of the robots 110 reactsdifferently to any single opponent 120.

Each of the robots 110 plays a game with each of the opponents 120, asshown at 507. For example, robot 110 with robot profile Ri plays againstan opponent 120 with opponent profile Hj. During the game, the robot 110predicts the opponent profile's Hj-i, as shown at 510. For example, therobot 110 generates a matrix, such as the 2 d matrix illustrated intable 1, using custom neural network services. When the game ends, therobot 110 reports the robot profile Ri, the opponent profile Hj-i (thatthe robot 110 generated), and the outcome of the game (win/lose), asshown at 520. The computer gaming system 100 ensures that each of therobots 110 generates and reports a corresponding opponent profile (e.g.Hj-1, Hj-2 . . . Hj-n), for each of the opponents 120 by ensuring thateach robot 110 plays each opponent 120 (n×m games, in the above example500), as shown at 530. If a game has not yet been played, the computergaming system 100 adjusts the values for i and/or j, as shown at 540.Once all the games have been played, the computer gaming system 100updates the mapping data 140 in the profile repository 130, as shown at550.

In one or more examples, the computer gaming system 100 maintains twoseparate lists in the profile repository, a first list includinginformation for games in which the opponent 120 lost against the robot110; and a second list including information for games in which theopponent 120 beat the robot 110. It should be noted that in games thatcan result in states different than win/lose, for example a tie (ordraw), the computer gaming system 100 maintains additional/differentlists in the mapping data 140 corresponding to the states of the gamebeing played.

The technical solutions described herein facilitate the computer gamingsystem 100 to configure the robot profile 115 of the robot 110 todefeat, or at least increase the chances of the robot 110 to defeat theopponent 120 using the mapping data 140.

FIG. 6 illustrates a flowchart of an example method for generating arobot profile using the mapping data, according to one or moreembodiments of the present invention. Consider that the opponent 120 isplaying against the robot 110. The computer gaming system 100 identifiesthe opponent profile 125, as shown at 610. In one or more examples, thecomputer gaming system 100 determines the opponent profile 125 for theopponent 120 playing against the robot 110 based on the unique opponentidentification. For example, the profile repository 130 maintains theopponent identification, such as a username, serial number, or any othersuch identification mark with each opponent profile 125. To continue theabove example that was used to describe the training of the robots 110,consider that the opponent 120 has the profile H41 and is playingagainst the robot 110 with the robot profile R5.

The computer gaming system 100 accesses the profile repository 130 toidentify and select a first list of robot profiles 115 that defeated theopponent profile 125 (H41), as shown at 620. Consider that [R1 R5 R7] isthe list of robot profiles that defeated the opponent with the profileH41 during the training phase above. This first list may also bereferred to as positive samples. The computer gaming system 100 furtheraccesses the profile repository 130 to identify and select another, asecond list of robot profiles 115 that were defeated by the opponentprofile 125 (H41), as shown 630. Consider that [R2 R3 R9 R10] is thelist of robot profiles that were defeated by H41. The second list mayalso be referred to as negative samples.

The computer gaming system 100 uses the positive samples and thenegative samples to generate a new robot profile that is more like thepositive samples, and lesser like the negative samples, as shown at 640.For example, the new robot profile is generated by feeding the positiveand negative samples into the custom built neural networks service, suchas in BLUEMIX™, or any other neural network service. For the descriptionherein, the new profile in this case is named R5_41, an adjusted robotprofile based on the robot profile R5 modified to compete againstopponent profile H41.

The robot 110 is configured to use the adjusted robot profile R5_41 tocompete against the opponent 120 with the opponent profile H41, as shownat 650. The robot 110 is thus trained to with the adjusted robot profileR5_41 to play against H41, as the robot 110 is more likely to defeat H41than any other personality. Once the game is completed, the robot 110updates the profile repository 130 with the outcome of the game betweenthe adjusted profile and the opponent profile H41, as shown at 660.

The updated includes updating the positive samples representing the‘defeated from robot data’ of H41 in the mapping data 140 as: [R5_41 R1R5 R7], if the robot 110 wins with the adjusted profile R5_41. Else, ifthe robot does not defeat H41 the computer gaming system 100 updates thenegative samples representing the ‘won from robot data’ of H41 in themapping data 140 as: [R5_41 R2 R3 R9 R10]. By updating the mapping data140 with new positive and negative samples for the opponent 120 with theopponent profile H41, the computer gaming system 100 improves theclassification accuracy for generating the next adjusted profile tocompete against H41, in long term.

The above method is used when the opponent 120 who is known to thecomputer gaming system 100 logs in to play the game. In one or moreexamples, the opponent 120, logs into the system using his/her usernameor other form of identification. The computer gaming system 100 proceedsto implement the above method in response to the opponent 120 having anexisting opponent profile 125.

FIG. 7 illustrates a flowchart of an example method for generating arobot profile for a new opponent, according to one or more embodimentsof the present invention. The computer gaming system 100 receives arequest to play against one of the robots 110, as shown at 710. Therequest is received from the opponent 120. The request includes anidentification of the opponent 120, such as a username, password, or anyother identifier or a combination thereof.

The computer gaming system 100 determines if the opponent 120 that isrequesting to play the game has corresponding opponent profile 125 withenough information in the mapping data 140, as shown at 720. In one ormore examples, the computer gaming system 100 determines that there isenough information in response to the profile repository 130 includingoutcomes of at least a predetermined number of games played by theopponent profile 125.

If the opponent profile 125 has at least the predetermined number ofoutcomes recorded in the mapping data 140 of the profile repository 130,the computer gaming system 100 generates an adjusted robot profile toplay against the opponent 120 based on the positive and negative samplesfrom the mapping data 140 (see FIG. 6), as shown at 725. In one or moreexamples, the computer gaming system 100 determines that there is enoughinformation if there are at least a first predetermined number ofpositive samples, and at least a second predetermined number of negativesamples in the mapping data 140. The first and second predeterminednumber is the same in one or more examples, and different in one or moreexamples. Accordingly, if the mapping data 140 includes at least thefirst and second predetermined number of positive and negative samplesrespectively, the computer gaming system 100 generates the robot profileto play against the opponent using the positive and negative samples.

Else, if the mapping data 140 does not have enough information, thecomputer gaming system 100 creates the opponent profile 125 by havingthe opponent 120 play against a set of predetermined robot profiles, asshown at 730 and 740. For example, the computer gaming system 100sequentially configures the robot 110 using one of the predeterminedrobot profiles from the predetermined set and has the robot 110 playagainst the opponent 120. The robot 110 captures information for theopponent profile 125 during the game play. The robot 110 also stores theoutcomes of the games using the predetermined set of robot profiles. Inone or more examples, the predetermined set of robot profiles has thesame number of predetermined robot profiles as is used to determinewhether the profile repository 130 has enough information about theopponent profile 125.

The computer gaming system 100 further categorizes the generatedopponent profile based on existing opponent profiles 125, as shown at750. Categorizing the generated opponent profile includes determiningclusters of existing opponent profiles 125 from the profile repository130, as shown at 752. For example, the clustering is performed usingk-means, or any other such algorithm using the opponent profilesmatrices. In one or more examples, the computer gaming system 100computes the centroids of each cluster that is identified. The centroidis another matrix that includes the computed values that the computergaming system 100 can interpret as an opponent profile.

The categorizing of the newly generated opponent profile furtherincludes identifying a cluster closest to the generated profile, asshown at 754. The closest cluster is determined by computing distancesbetween the newly generated opponent profile and the centroids of theclusters. The distances computed are in Cartesian coordinate system, inone or more examples. Alternatively, the Euclidean coordinate system isused. Any other coordinate system can be used in other examples. Thesame coordinate system used when clustering the existing opponentprofiles 125 is used when determining the distance between the newlygenerated opponent profile and the centroids of the clusters.

The categorizing further includes selecting the centroid of the closestcluster, as shown at 756. The selected centroid is then used torepresent the opponent profile 125 of the opponent 120 that requestedthe game play. The computer gaming system 100 further generates therobot profile 115 for the robot 110 to play the opponent 120 accordingto mapping data for the selected centroid, as shown at 760.

In one or more examples, the opponent profiles 125 stored in the profilerepository 130 are from the multiple robots from the computer gamingsystem 100. Accordingly, a first robot uses opponent profile and outcomestored by a second robot, and vice versa when the corresponding opponentrequests to play against one of those robots. Each robot 110 is anindependent AI system/device in itself that stores the outcome ofplaying a game with the opponent 120 in the profile repository 130 andupdates the mapping data 140 accordingly.

Thus, the computer gaming system 100 described herein facilitatesindependent AI devices defeat opponents, such as humans in games likepoker, chess etc. The collaboration overcomes technical challenges facedwhen generating robot profiles for AI devices to play a game against ahuman opponent. For example, defeating someone in a game is not atransitive relation; that is, if A defeats B and B defeats C, then itdoes not mean that A will defeat C. This happens because defeatingsomeone not only includes the game strategy, but also body language,behavior, aggressiveness, and other such factors that can discourage agood player such that s/he starts losing in the game. The technicalsolutions herein address such technical challenges by convertingpersonality data into computer readable data in order to defeat theopponents. Further, the technical solutions use a combination of gamestrategy and the human personality (both defined in mathematicalcomputer readable form) in order to train or configure a robot andevolve into a personality that can defeat the opponent. The technicalsolutions also facilitate defeating a human opponent consideringone-to-one relations between two players (instead of transitiverelations), and uses the relations to create the evolved personality forthe robot playing the opponent, so that the robot is more likely todefeat the opponent (human).

Thus, the technical solutions facilitate intelligence sharing betweenindependent AI devices to collaborate and defeat a human opponent basedon respective experience. The technical solutions further facilitateinferring and using personality trait in addition to game strategy byusing clustering and neural networks to train an AI device (robot) todemonstrate a random personality trait, such as aggression, extraversionetc., different from what the human opponent is expecting from therobot, in order to confuse the human opponent in an attempt to defeatthe human opponent with an evolved game strategy. Further, the technicalsolutions facilitate the independent AI devices collaborate about gamestrategy and further about personality traits of a human opponent toadapt gameplay. The technical solutions facilitate determining gamestrategy intelligence based on in-game actions that are associated withthe gameplay, and further determining the personality traits based onout-of-game actions of the human opponent, such as speech, facialexpressions, perspiration, hand movements, and the like. The technicalsolutions thus facilitate a computer game system to adapt to differenthuman opponents with different personalities, by generating differentrobot profiles to compete against the human opponents. The technicalsolutions thus provide an improvement to computer technology byfacilitating determination of the personality traits in computerreadable form, and further sharing the determined personality traitsacross multiple independent AI devices (robots) to train a robot tocompete against a human opponent with the determined personality traits.The technical solutions accordingly provide techniques, such as rulesfor operation of a computer system, such as a computer game system, tooperate and train an AI device.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instruction by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdescribed herein.

What is claimed is:
 1. A computer-implemented method comprising:identifying an electronic opponent profile for an opponent; selecting,from a profile repository, a first set of robot profiles, wherein robotstrained using robot profiles from the first set of robot profiles havepreviously defeated the opponent; selecting, from the profilerepository, a second set of robot profiles, wherein robots trained usingrobot profiles from the second set of robot profiles have previouslylost to the opponent; generating a current robot profile based on thefirst set of robot profiles and the second robot profiles; andconfiguring a robot according to the current robot profile to playagainst the opponent.
 2. The computer-implemented method of claim 1,further comprising: reporting the current profile and outcome of a gamebetween the robot and the opponent to the profile repository.
 3. Thecomputer-implemented method of claim 1, wherein the electronic opponentprofile is identified using an identification of the opponent.
 4. Thecomputer-implemented method of claim 1, wherein the electronic opponentprofile comprises risk-parameters associated with the opponent.
 5. Thecomputer-implemented method of claim 1, wherein the electronic opponentprofile comprises personality traits scores associated with theopponent.
 6. The computer-implemented method of claim 5, furthercomprising: recording out-of-game actions of the opponent; anddetermining the personality traits scores based on the out-of-gameactions of the opponent.
 7. The computer-implemented method of claim 5,further comprising: computing a range of risk-parameters of the robotbased on the personality traits scores of the opponent; and storing therange of risk-parameters of the robot in the current robot profile. 8.The computer-implemented method of claim 7, further comprising:selecting an input value within the range of risk-parameters from thecurrent robot profile; determining an in-game action for the robot basedon the selected input value; and making the in-game action by the robot.9. The computer-implemented method of claim 5, wherein the personalitytraits scores associated with the opponent represent at least one from agroup consisting of aggressiveness, extraversion, openness, neuroticism,agreeableness, and conscientiousness.
 10. A computer-game systemcomprising: a memory; a robot configured to play a game against anopponent; and a processor coupled with the memory and the robot, theprocessor configured to: identify an electronic opponent profile for theopponent; select, from a profile repository, a first set of robotprofiles, wherein robots trained using robot profiles from the first setof robot profiles have previously defeated the opponent; select, fromthe profile repository, a second set of robot profiles, wherein robotstrained using robot profiles from the second set of robot profiles havepreviously lost to the opponent; generate a current robot profile basedon the first set of robot profiles and the second robot profiles; andconfigure the robot according to the current robot profile to playagainst the opponent.
 11. The computer-game system of claim 10, theprocessor further configured to: report the current profile and outcomeof a game between the robot and the opponent to the profile repository.12. The computer-game system of claim 10, wherein the electronicopponent profile is identified using an identification of the opponent.13. The computer-game system of claim 10, wherein the electronicopponent profile comprises risk-parameters associated with the opponent.14. The computer-game system of claim 10, wherein the electronicopponent profile comprises personality traits scores associated with theopponent.
 15. The computer-game system of claim 14, the processorfurther configured to: record out-of-game actions of the opponent; anddetermine the personality traits scores based on the out-of-game actionsof the opponent.
 16. The computer-game system of claim 15, the processorfurther configured to: compute a range of risk-parameters of the robotbased on the personality traits scores of the opponent; and store therange of risk-parameters of the robot in the current robot profile. 17.The computer-game system of claim 16, the processor further configuredto: selecting an input value within the range of risk-parameters fromthe current robot profile; determining an in-game action for the robotbased on the selected input value; and making the in-game action by therobot.
 18. A computer program product for training a robot for gameplay,the computer program product comprising a computer readable storagemedium having program instructions embodied therewith, the programinstructions executable by a processing circuit to cause the processingcircuit to: identify an electronic opponent profile for an opponent;select, from a profile repository, a first set of robot profiles,wherein robots trained using robot profiles from the first set of robotprofiles have previously defeated the opponent; select, from the profilerepository, a second set of robot profiles, wherein robots trained usingrobot profiles from the second set of robot profiles have previouslylost to the opponent; generate a current robot profile based on thefirst set of robot profiles and the second robot profiles; and configurea robot according to the current robot profile to play against theopponent.
 19. The computer program product of claim 18, wherein theelectronic opponent profile comprises personality traits scoresassociated with the opponent.
 20. The computer program product of claim19, the program instructions further cause the processing circuit to:record out-of-game actions of the opponent; and determine thepersonality traits scores based on the out-of-game actions of theopponent.